This research focuses on developing AI-enhanced predictive maintenance (PdM) strategies for critical wastewater treatment plant (WWTP) equipment such as decanter centrifuges and turbo blowers. By leveraging machine learning models and real-time operational data, the project aims to predict equipment failures, optimize maintenance schedules, and minimize unplanned downtime. The approach will improve system reliability, reduce operational costs, and support smarter, data-driven maintenance decisions in WWTPs. (Xiangyuan)